Applied Machine Learning Explainability Techniques

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Applied Machine Learning Explainability Techniques

Aditya Bhattacharya
Applied Machine Learning Explainability Techniques Book PDF

Author : Aditya Bhattacharya
Publisher : Packt Publishing Ltd
Page : 306 pages
File Size : 53,8 Mb
Release : 2022-07-29
Category : Computers
ISBN : 9781803234168

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Applied Machine Learning Explainability Techniques by Aditya Bhattacharya Pdf

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various explainability methods for designing robust and scalable explainable ML systems • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems • Design user-centric explainable ML systems using guidelines provided for industrial applications Book Description Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn • Explore various explanation methods and their evaluation criteria • Learn model explanation methods for structured and unstructured data • Apply data-centric XAI for practical problem-solving • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others • Discover industrial best practices for explainable ML systems • Use user-centric XAI to bring AI closer to non-technical end users • Address open challenges in XAI using the recommended guidelines Who this book is for This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

Explainable AI: Foundations, Methodologies and Applications

Mayuri Mehta,Vasile Palade,Indranath Chatterjee
Explainable AI Foundations Methodologies and Applications Book PDF

Author : Mayuri Mehta,Vasile Palade,Indranath Chatterjee
Publisher : Springer Nature
Page : 273 pages
File Size : 53,6 Mb
Release : 2022-10-19
Category : Technology & Engineering
ISBN : 9783031128073

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Explainable AI: Foundations, Methodologies and Applications by Mayuri Mehta,Vasile Palade,Indranath Chatterjee Pdf

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Explainable AI with Python

Leonida Gianfagna,Antonio Di Cecco
Explainable AI with Python Book PDF

Author : Leonida Gianfagna,Antonio Di Cecco
Publisher : Springer Nature
Page : 202 pages
File Size : 52,6 Mb
Release : 2021-04-28
Category : Computers
ISBN : 9783030686406

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Explainable AI with Python by Leonida Gianfagna,Antonio Di Cecco Pdf

This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 40,7 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Explainable AI for Practitioners

Michael Munn,David Pitman
Explainable AI for Practitioners Book PDF

Author : Michael Munn,David Pitman
Publisher : "O'Reilly Media, Inc."
Page : 279 pages
File Size : 40,5 Mb
Release : 2022-10-31
Category : Computers
ISBN : 9781098119102

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Explainable AI for Practitioners by Michael Munn,David Pitman Pdf

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

Applied Machine Learning for Healthcare and Life Sciences Using AWS

Ujjwal Ratan
Applied Machine Learning for Healthcare and Life Sciences Using AWS Book PDF

Author : Ujjwal Ratan
Publisher : Packt Publishing Ltd
Page : 224 pages
File Size : 41,9 Mb
Release : 2022-11-25
Category : Computers
ISBN : 9781804619193

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Applied Machine Learning for Healthcare and Life Sciences Using AWS by Ujjwal Ratan Pdf

Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizations Key FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook Description While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence. What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is for This book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.

Production-Ready Applied Deep Learning

Tomasz Palczewski,Jaejun (Brandon) Lee,Lenin Mookiah
Production Ready Applied Deep Learning Book PDF

Author : Tomasz Palczewski,Jaejun (Brandon) Lee,Lenin Mookiah
Publisher : Packt Publishing Ltd
Page : 322 pages
File Size : 44,8 Mb
Release : 2022-08-30
Category : Computers
ISBN : 9781803238050

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Production-Ready Applied Deep Learning by Tomasz Palczewski,Jaejun (Brandon) Lee,Lenin Mookiah Pdf

Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deployment Book Description Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting. What you will learn Understand how to develop a deep learning model using PyTorch and TensorFlow Convert a proof-of-concept model into a production-ready application Discover how to set up a deep learning pipeline in an efficient way using AWS Explore different ways to compress a model for various deployment requirements Develop Android and iOS applications that run deep learning on mobile devices Monitor a system with a deep learning model in production Choose the right system architecture for developing and deploying a model Who this book is for Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Uday Kamath,John Liu
Explainable Artificial Intelligence An Introduction to Interpretable Machine Learning Book PDF

Author : Uday Kamath,John Liu
Publisher : Springer
Page : 310 pages
File Size : 52,7 Mb
Release : 2021-12-16
Category : Computers
ISBN : 3030833550

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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning by Uday Kamath,John Liu Pdf

This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group

Hands-On Explainable AI (XAI) with Python

Denis Rothman
Hands On Explainable AI XAI with Python Book PDF

Author : Denis Rothman
Publisher : Packt Publishing Ltd
Page : 455 pages
File Size : 46,5 Mb
Release : 2020-07-31
Category : Computers
ISBN : 9781800202764

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Hands-On Explainable AI (XAI) with Python by Denis Rothman Pdf

Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications

Applied Machine Learning for Assisted Living

Zia Uddin
Applied Machine Learning for Assisted Living Book PDF

Author : Zia Uddin
Publisher : Springer Nature
Page : 139 pages
File Size : 47,6 Mb
Release : 2022-08-29
Category : Medical
ISBN : 9783031115349

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Applied Machine Learning for Assisted Living by Zia Uddin Pdf

User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers of the related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.

Applied Geospatial Data Science with Python

David S. Jordan
Applied Geospatial Data Science with Python Book PDF

Author : David S. Jordan
Publisher : Packt Publishing Ltd
Page : 308 pages
File Size : 40,8 Mb
Release : 2023-02-28
Category : Computers
ISBN : 9781803240343

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Applied Geospatial Data Science with Python by David S. Jordan Pdf

Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.

Applied Machine Learning

David Forsyth
Applied Machine Learning Book PDF

Author : David Forsyth
Publisher : Springer
Page : 0 pages
File Size : 54,9 Mb
Release : 2020-08-14
Category : Computers
ISBN : 3030181162

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Applied Machine Learning by David Forsyth Pdf

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller
Explainable AI Interpreting Explaining and Visualizing Deep Learning Book PDF

Author : Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller
Publisher : Springer Nature
Page : 435 pages
File Size : 40,6 Mb
Release : 2019-09-10
Category : Computers
ISBN : 9783030289546

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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller Pdf

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

The AI Product Manager's Handbook

Irene Bratsis
The AI Product Manager s Handbook Book PDF

Author : Irene Bratsis
Publisher : Packt Publishing Ltd
Page : 250 pages
File Size : 54,7 Mb
Release : 2023-02-28
Category : Computers
ISBN : 9781804617335

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The AI Product Manager's Handbook by Irene Bratsis Pdf

Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and commercial success Take macro data and use it to show your customers you’re a source of truth Best practices and common pitfalls that impact companies while developing AI product Book DescriptionProduct managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You’ll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You’ll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you’ll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you’ll have understood how to navigate the world of AI from a product perspective.What you will learn Build AI products for the future using minimal resources Identify opportunities where AI can be leveraged to meet business needs Collaborate with cross-functional teams to develop and deploy AI products Analyze the benefits and costs of developing products using ML and DL Explore the role of ethics and responsibility in dealing with sensitive data Understand performance and efficacy across verticals Who this book is for This book is for product managers and other professionals interested in incorporating AI into their products. Foundational knowledge of AI is expected. If you understand the importance of AI as the rising fourth industrial revolution, this book will help you surf the tidal wave of digital transformation and change across industries.

Explainable AI with Python

Leonida Gianfagna,Antonio Di Cecco
Explainable AI with Python Book PDF

Author : Leonida Gianfagna,Antonio Di Cecco
Publisher : Unknown
Page : 0 pages
File Size : 45,7 Mb
Release : 2021
Category : Electronic
ISBN : 3030686418

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Explainable AI with Python by Leonida Gianfagna,Antonio Di Cecco Pdf

This book provides a full presentation of the current concepts and available techniques to make "machine learning" systems more explainable. The approaches presented can be applied to almost all the current "machine learning" models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI.